The NLP Architect requires Python 3.5+ running on a Linux* or UNIX-based OS (like Mac OS). We recommend using the library with Ubuntu 16.04+.

Before installing the library make sure you has the most recent packages listed below:

Ubuntu* 16.04+ or CentOS* 7.4+ Mac OS X* Description
python-pip pip Tool to install Python dependencies
libhdf5-dev h5py Enables loading of hdf5 formats
pkg-config pkg-config Retrieves information about installed libraries


The default installation of NLP Architect use CPU-based binaries of all deep learning frameworks. Intel Optimized MKL-DNN binaries will be installed if a Linux is detected. GPU backed is supported online on Linux and if a GPU is present. See details below for instructions on how to install each backend.



Make sure pip and setuptools and venv are up to date before installing.

pip3 install -U pip setuptools venv

We recommend installing NLP Architect in a virtual environment to self-contain the work done using the library.

To create and activate a new virtual environment (or skip this step and use the wizard below):

python3 -m venv .nlp_architect_env
source .nlp_architect_env/bin/activate

Quick Install

Select the desired configuration of your system:

Install from
Create virtualenv?
Install in developer mode?

Run the following commands to install NLP Architect:

It is recommended to install NLP Architect in development mode to utilize all its features, examples and solutions.

Install from source

To get started, clone our repository:

git clone
cd nlp-architect

Selecting a backend

NLP Architect supports CPU, GPU and Intel Optimized Tensorflow (MKL-DNN) backends. Users can select the desired backend using a dedicated environment variable (default: CPU). (MKL-DNN and GPU backends are supported only on Linux)



NLP Architect is installed using pip and it is recommended to install in development mode.


pip3 install .

Development mode:

pip3 install -e .

Once installed, the nlp_architect command provides additional options to work with the library, issue nlp_architect -h to see all options.

Compiling Intel® optimized Tensorflow with MKL-DNN

NLP Architect supports MKL-DNN flavor of Tensorflow out of the box, however, if the user wishes to compile Tensorflow we provide instructions below.

Tensorflow has a guide guide for compiling and installing Tensorflow with with MKL-DNN optimization. Make sure to install all required tools: bazel and python development dependencies.

Alternatively, follow the instructions below to compile and install the latest version of Tensorflow with MKL-DNN:

  • Clone Tensorflow repository from GitHub:

    git clone
    cd tensorflow
  • Configure Tensorflow for compilation:

  • Compile Tensorflow with MKL-DNN:

    bazel build --config=mkl --config=opt //tensorflow/tools/pip_package:build_pip_package
  • Create pip package in /tmp/tensorflow_pkg:

    bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
  • Install Tensorflow pip package:

    pip install <tensorflow package name>.whl
  • Refer to this guide for specific configuration to get optimal performance when running your model.